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Water Transparency Prediction of Plain Urban River Network: A Case Study of Yangtze River Delta in China

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  • Yipeng Liao

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
    College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China)

  • Yun Li

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China)

  • Jingxiang Shu

    (Department of Civil and Environmental Engineering, The University of Auckland, Auckland Mail Centre, Private Bag 92019, Auckland 1142, New Zealand)

  • Zhiyong Wan

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
    School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China)

  • Benyou Jia

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China)

  • Ziwu Fan

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China)

Abstract

Water transparency is commonly used to indicate the combined effect of hydrodynamics and the aquatic environment on water quality throughout a river network. However, how water transparency responds to these indicators still needs to be explored, especially their complicated nonlinear relationship; thus, this study represents an analysis of the Suzhou civil river network. Using an artificial neural network (ANN) hydrological model and a multiple linear model (MLR) with in-situ data between 2013–2019, we investigated the Suzhou River’s sensitivity to the six factors and water transparency, which including flow velocity and data from five categories of water-quality monitoring data: total suspended matter (TSS), water temperature (TE), dissolved oxygen (DO), chlorophyll (Chl) and chemical oxygen demand (COD). The results suggest that the ANN model can achieve better performance than the MLR model. Furthermore, results also show a well-established correlation between enhanced hydrodynamics and improved water transparency when the flow velocity ranged from 0.22 to 0.45 m/s. Overall, COD is a vital factor for the SD prediction because including the COD can see a notable improvement in the ANN model (with a correlation coefficient of 0.918). This study demonstrates that the ANN model with hydrodynamic and water quality parameters can achieve a better prediction of water transparency than other discussed models for a coastal plain urban river network.

Suggested Citation

  • Yipeng Liao & Yun Li & Jingxiang Shu & Zhiyong Wan & Benyou Jia & Ziwu Fan, 2021. "Water Transparency Prediction of Plain Urban River Network: A Case Study of Yangtze River Delta in China," Sustainability, MDPI, vol. 13(13), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7372-:d:586535
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    References listed on IDEAS

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    1. Guofeng Wu & Jan Leeuw & Yaolin Liu, 2009. "Understanding Seasonal Water Clarity Dynamics of Lake Dahuchi from In Situ and Remote Sensing Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(9), pages 1849-1861, July.
    2. Chung, Eu Gene & Bombardelli, Fabián A. & Schladow, S. Geoffrey, 2009. "Modeling linkages between sediment resuspension and water quality in a shallow, eutrophic, wind-exposed lake," Ecological Modelling, Elsevier, vol. 220(9), pages 1251-1265.
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    Cited by:

    1. Rui Ding & Kai Yu & Ziwu Fan & Jiaying Liu, 2022. "Study and Application of Urban Aquatic Ecosystem Health Evaluation Index System in River Network Plain Area," IJERPH, MDPI, vol. 19(24), pages 1-11, December.
    2. Qiuying Lai & Jie Ma & Fei He & Geng Wei, 2022. "Response Model for Urban Area Source Pollution and Water Environmental Quality in a River Network Region," IJERPH, MDPI, vol. 19(17), pages 1-14, August.

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